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LAIIER’s Severn WLD smart water leak detection system provides an alternative to older flood, inline, and cable/rope sensors for detecting leaks and the presence of water in smart building, datacentre, agricultural, white goods, and industrial applications.
Thanks to an innovative composition of PET tape with an adhesive backing and printed resistive tracks configured as multiple electrodes, Severn WLD features a unique form factor that can be easily installed within minutes. This allows leak monitoring in hard-to-reach places and over large areas while retaining high-sensitivity, localised detection capabilities.
The default sensor has a length of 1 m with 12 electrodes/regions. Each electrode within the sensor can detect as little as 2 drops of water, and intelligent tracking of the spread of water over multiple electrodes allows the user to improve operational efficiency within a facility. Users can determine the location and severity of leaks and act on this information accordingly – a small leak in a server room is more concerning than a small leak in a shower room, for example – and different detection criteria can be selected in order to trigger an alarm response, reducing the number of false positives.
For scaling this solution for deployments in large buildings, LAIIER’s sensing tape is connected to a battery-powered hardware module that features LoRaWAN connectivity for remote monitoring. Data and predictive insights can be viewed using LAIIER’s cloud-based dashboard for an end-to-end monitoring solution.
In order to test this technology, LAIIER offers demos and a variety of evaluation platforms. These include a sensor setup with data output via USB to test the sensing tape itself, a single LoRaWAN device (pictured) for users who already have a LoRaWAN setup, and a multiple-unit kit that includes a LoRaWAN gateway to test a use case with no prior infrastructure.
If you would like to learn more, fill in the form below, and ipXchange will put you in touch with LAIIER to discuss your project and facilitate evaluation.